Authors:
Uma Subbiah
1
;
Muthu Ramachandran
2
and
Zaigham Mahmood
3
Affiliations:
1
Computer Science and Engineering, Amrita School of Engineering, Amritanagar, Ettimadai, 641112, Coimbatore, Tamil Nadu and India
;
2
School of Computing, Creative Technologies & Engineering, Leeds Beckett University, Headingley Campus, Churchwood Ave, LS6 3QS, Leeds and U.K.
;
3
Computing, Research, University of Derby, Kedleston Rd, Ilkeston, DE22 1GB, Derby and U.K.
Keyword(s):
Machine Learning, Machine Learning as a Service, Bug Prediction, Bug Prediction as a Service, Microsoft Azure.
Abstract:
The presence of bugs in a software release has become inevitable. The loss incurred by a company due to the presence of bugs in a software release is phenomenal. Modern methods of testing and debugging have shifted focus from “detecting” to “predicting” bugs in the code. The existing models of bug prediction have not been optimized for commercial use. Moreover, the scalability of these models has not been discussed in depth yet. Taking into account the varying costs of fixing bugs, depending on which stage of the software development cycle the bug is detected in, this paper uses two approaches – one model which can be employed when the ‘cost of changing code’ curve is exponential and the other model can be used otherwise. The cases where each model is best suited are discussed. This paper proposes a model that can be deployed on a cloud platform for software development companies to use. The model in this paper aims to predict the presence or absence of a bug in the code, using machi
ne learning classification models. Using Microsoft Azure’s machine learning platform this model can be distributed as a web service worldwide, thus providing Bug Prediction as a Service (BPaaS).
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